{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:QDEZ6XDFQ6WFQMFJEFWCW2G3BJ","short_pith_number":"pith:QDEZ6XDF","schema_version":"1.0","canonical_sha256":"80c99f5c6587ac5830a9216c2b68db0a7546145a73271beaa6bce098ce76970e","source":{"kind":"arxiv","id":"1903.02500","version":1},"attestation_state":"computed","paper":{"title":"Prostate Segmentation from 3D MRI Using a Two-Stage Model and Variable-Input Based Uncertainty Measure","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Craig Meyer, Huitong Pan, Quan Chen, Xue Feng, Yushan Feng","submitted_at":"2019-03-06T17:18:33Z","abstract_excerpt":"This paper proposes a two-stage segmentation model, variable-input based uncertainty measures and an uncertainty-guided post-processing method for prostate segmentation on 3D magnetic resonance images (MRI). The two-stage model was based on 3D dilated U-Nets with the first stage to localize the prostate and the second stage to obtain an accurate segmentation from cropped images. For data augmentation, we proposed the variable-input method which crops the region of interest with additional random variations. Similar to other deep learning models, the proposed model also faced the challenge of s"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1903.02500","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-06T17:18:33Z","cross_cats_sorted":[],"title_canon_sha256":"151efd6c5c1deaf934fa2ad5bf4adb56131e90782ae6499f4b6809c02ff64bad","abstract_canon_sha256":"a48d97f51f53e11a99059752111300a038dca77d4c253cd92b06e40ae7eb1765"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:51:55.221079Z","signature_b64":"EDI99zj/QC+sk5J3wAQ2b7NycDGtvXPz6NMficeddTb0I0bHA8tvimDfc9jB6NqBzWUynoUl5LD4L3EGLYXhAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"80c99f5c6587ac5830a9216c2b68db0a7546145a73271beaa6bce098ce76970e","last_reissued_at":"2026-05-17T23:51:55.220578Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:51:55.220578Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Prostate Segmentation from 3D MRI Using a Two-Stage Model and Variable-Input Based Uncertainty Measure","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Craig Meyer, Huitong Pan, Quan Chen, Xue Feng, Yushan Feng","submitted_at":"2019-03-06T17:18:33Z","abstract_excerpt":"This paper proposes a two-stage segmentation model, variable-input based uncertainty measures and an uncertainty-guided post-processing method for prostate segmentation on 3D magnetic resonance images (MRI). The two-stage model was based on 3D dilated U-Nets with the first stage to localize the prostate and the second stage to obtain an accurate segmentation from cropped images. For data augmentation, we proposed the variable-input method which crops the region of interest with additional random variations. Similar to other deep learning models, the proposed model also faced the challenge of s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.02500","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1903.02500","created_at":"2026-05-17T23:51:55.220661+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.02500v1","created_at":"2026-05-17T23:51:55.220661+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.02500","created_at":"2026-05-17T23:51:55.220661+00:00"},{"alias_kind":"pith_short_12","alias_value":"QDEZ6XDFQ6WF","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_16","alias_value":"QDEZ6XDFQ6WFQMFJ","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_8","alias_value":"QDEZ6XDF","created_at":"2026-05-18T12:33:27.125529+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QDEZ6XDFQ6WFQMFJEFWCW2G3BJ","json":"https://pith.science/pith/QDEZ6XDFQ6WFQMFJEFWCW2G3BJ.json","graph_json":"https://pith.science/api/pith-number/QDEZ6XDFQ6WFQMFJEFWCW2G3BJ/graph.json","events_json":"https://pith.science/api/pith-number/QDEZ6XDFQ6WFQMFJEFWCW2G3BJ/events.json","paper":"https://pith.science/paper/QDEZ6XDF"},"agent_actions":{"view_html":"https://pith.science/pith/QDEZ6XDFQ6WFQMFJEFWCW2G3BJ","download_json":"https://pith.science/pith/QDEZ6XDFQ6WFQMFJEFWCW2G3BJ.json","view_paper":"https://pith.science/paper/QDEZ6XDF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.02500&json=true","fetch_graph":"https://pith.science/api/pith-number/QDEZ6XDFQ6WFQMFJEFWCW2G3BJ/graph.json","fetch_events":"https://pith.science/api/pith-number/QDEZ6XDFQ6WFQMFJEFWCW2G3BJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QDEZ6XDFQ6WFQMFJEFWCW2G3BJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QDEZ6XDFQ6WFQMFJEFWCW2G3BJ/action/storage_attestation","attest_author":"https://pith.science/pith/QDEZ6XDFQ6WFQMFJEFWCW2G3BJ/action/author_attestation","sign_citation":"https://pith.science/pith/QDEZ6XDFQ6WFQMFJEFWCW2G3BJ/action/citation_signature","submit_replication":"https://pith.science/pith/QDEZ6XDFQ6WFQMFJEFWCW2G3BJ/action/replication_record"}},"created_at":"2026-05-17T23:51:55.220661+00:00","updated_at":"2026-05-17T23:51:55.220661+00:00"}